21 citations · 40 across the 14 of their papers we have counts for
11 papers · 1 filter
Spectral clustering of network time series via the sample covariance matrix
Brendan Martin, Joshua Agterberg, Mihai Cucuringu +2
Spectral clustering for community detection is analysed in multivariate time series models whose dependence structure is determined by an unobserved stochastic blockmodel. We estab…
Statistically and Computationally Optimal Estimation and Inference of Common Subspaces
Joshua Agterberg
Given multiple data matrices, many problems in statistics and data science rely on estimating a common subspace that captures certain structure shared by all the data matrices. In…
Statistical Inference for Low-Rank Tensors: Heteroskedasticity, Subgaussianity, and Applications
Joshua Agterberg, Anru Zhang
In this paper, we consider inference and uncertainty quantification for low Tucker rank tensors with additive noise in the high-dimensional regime. Focusing on the output of the hi…
Statistical Inference for Linear Functions of Eigenvectors with Small Eigengaps
Joshua Agterberg
Spectral methods have myriad applications in high-dimensional statistics and data science, and while previous works have primarily focused on or eigenvec…
An Overview of Asymptotic Normality in Stochastic Blockmodels: Cluster Analysis and Inference
Joshua Agterberg, Joshua Cape
This paper provides a selective review of the statistical network analysis literature focused on clustering and inference problems for stochastic blockmodels and their variants. We…
Estimating Higher-Order Mixed Memberships via the Tensor Perturbation Bound
Joshua Agterberg, Anru Zhang
Higher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has…
Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels
Joshua Agterberg, Zachary Lubberts, Jesús Arroyo
Modern network datasets are often composed of multiple layers, either as different views, time-varying observations, or independent sample units, resulting in collections of networ…
Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms
Joshua Agterberg, Jeremias Sulam
Sparse Principal Component Analysis (PCA) is a prevalent tool across a plethora of subfields of applied statistics. While several results have characterized the recovery error of t…
Entrywise Estimation of Singular Vectors of Low-Rank Matrices with Heteroskedasticity and Dependence
Joshua Agterberg, Zachary Lubberts, Carey Priebe
We propose an estimator for the singular vectors of high-dimensional low-rank matrices corrupted by additive subgaussian noise, where the noise matrix is allowed to have dependence…
Nonparametric two-sample hypothesis testing for low-rank random graphs of differing sizes
Joshua Agterberg, Minh Tang, Carey Priebe
Given two networks of differing sizes, it is of interest to test whether the two networks belong to the same distribution. We formalize the notion of "equality of distribution" und…
On Two Distinct Sources of Nonidentifiability in Latent Position Random Graph Models
Joshua Agterberg, Minh Tang, Carey E. Priebe
Two separate and distinct sources of nonidentifiability arise naturally in the context of latent position random graph models, though neither are unique to this setting. In this pa…